{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with DoWhy: A simple example\n",
    "This is a quick introduction to the DoWhy causal inference library.\n",
    "We will load in a sample dataset and estimate the causal effect of a (pre-specified) treatment variable on a (pre-specified) outcome variable.\n",
    "\n",
    "First, let us load all required packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from dowhy import CausalModel\n",
    "import dowhy.datasets \n",
    "\n",
    "# Avoid printing dataconversion warnings from sklearn and numpy\n",
    "import warnings\n",
    "from sklearn.exceptions import DataConversionWarning\n",
    "warnings.filterwarnings(action='ignore', category=DataConversionWarning)\n",
    "warnings.filterwarnings(action='ignore', category=FutureWarning)\n",
    "\n",
    "# Config dict to set the logging level\n",
    "import logging\n",
    "import logging.config\n",
    "DEFAULT_LOGGING = {\n",
    "    'version': 1,\n",
    "    'disable_existing_loggers': False,\n",
    "    'loggers': {\n",
    "        '': {\n",
    "            'level': 'WARN',\n",
    "        },\n",
    "    }\n",
    "}\n",
    "\n",
    "logging.config.dictConfig(DEFAULT_LOGGING)\n",
    "logging.info(\"Getting started with DoWhy. Running notebook...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let us load a dataset. For simplicity, we simulate a dataset with linear relationships between common causes and treatment, and common causes and outcome. \n",
    "\n",
    "Beta is the true causal effect. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         X0   Z0        Z1        W0        W1        W2        W3 W4    v0  \\\n",
      "0  1.813045  0.0  0.386437  1.737406 -0.770319  0.368265  2.265652  2  True   \n",
      "1  0.289600  1.0  0.330004  2.005843 -0.816153 -0.963184 -1.939368  2  True   \n",
      "2  0.882267  0.0  0.728241  0.964749 -1.786494 -0.239238  0.194021  0  True   \n",
      "3  2.002775  1.0  0.865513  2.773636 -0.751109 -1.085667 -1.385620  3  True   \n",
      "4  2.053132  0.0  0.327094  0.120475 -0.370037  2.716818  1.185396  1  True   \n",
      "\n",
      "           y  \n",
      "0  23.426899  \n",
      "1  -4.980316  \n",
      "2   3.646363  \n",
      "3  -0.890903  \n",
      "4  30.815931  \n",
      "digraph { U[label=\"Unobserved Confounders\"]; U->y;v0->y;U->v0;W0-> v0; W1-> v0; W2-> v0; W3-> v0; W4-> v0;Z0-> v0; Z1-> v0;W0-> y; W1-> y; W2-> y; W3-> y; W4-> y;X0-> y;}\n",
      "\n",
      "\n",
      "graph[directed 1node[ id \"y\" label \"y\"]node[ id \"Unobserved Confounders\" label \"Unobserved Confounders\"]edge[source \"Unobserved Confounders\" target \"y\"]node[ id \"W0\" label \"W0\"] node[ id \"W1\" label \"W1\"] node[ id \"W2\" label \"W2\"] node[ id \"W3\" label \"W3\"] node[ id \"W4\" label \"W4\"]node[ id \"Z0\" label \"Z0\"] node[ id \"Z1\" label \"Z1\"]node[ id \"v0\" label \"v0\"]edge[source \"Unobserved Confounders\" target \"v0\"]edge[source \"v0\" target \"y\"]edge[ source \"W0\" target \"v0\"] edge[ source \"W1\" target \"v0\"] edge[ source \"W2\" target \"v0\"] edge[ source \"W3\" target \"v0\"] edge[ source \"W4\" target \"v0\"]edge[ source \"Z0\" target \"v0\"] edge[ source \"Z1\" target \"v0\"]edge[ source \"W0\" target \"y\"] edge[ source \"W1\" target \"y\"] edge[ source \"W2\" target \"y\"] edge[ source \"W3\" target \"y\"] edge[ source \"W4\" target \"y\"]node[ id \"X0\" label \"X0\"] edge[ source \"X0\" target \"y\"]]\n"
     ]
    }
   ],
   "source": [
    "data = dowhy.datasets.linear_dataset(beta=10,\n",
    "        num_common_causes=5,\n",
    "        num_instruments = 2,\n",
    "        num_effect_modifiers=1,\n",
    "        num_samples=5000, \n",
    "        treatment_is_binary=True,\n",
    "        stddev_treatment_noise=10,\n",
    "        num_discrete_common_causes=1)\n",
    "df = data[\"df\"]\n",
    "print(df.head())\n",
    "print(data[\"dot_graph\"])\n",
    "print(\"\\n\")\n",
    "print(data[\"gml_graph\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we are using a pandas dataframe to load the data. At present, DoWhy only supports pandas dataframe as input."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interface 1 (recommended): Input causal graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now input a causal graph in the GML graph format (recommended). You can also use the DOT format.\n",
    "\n",
    "To create the causal graph for your dataset, you can use a tool like [DAGitty](http://dagitty.net/dags.html#) that provides a GUI to construct the graph. You can export the graph string that it generates. The graph string is very close to the DOT format: just rename `dag` to `digraph`, remove newlines and add a semicolon after every line, to convert it to the DOT format and input to DoWhy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With graph\n",
    "model=CausalModel(\n",
    "        data = df,\n",
    "        treatment=data[\"treatment_name\"],\n",
    "        outcome=data[\"outcome_name\"],\n",
    "        graph=data[\"gml_graph\"]\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.view_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "display(Image(filename=\"causal_model.png\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above causal graph shows the assumptions encoded in the causal model. We can now use this graph to first identify \n",
    "the causal effect (go from a causal estimand to a probability expression), and then estimate the causal effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DoWhy philosophy: Keep identification and estimation separate\n",
    "\n",
    "Identification can be achieved without access to the data, acccesing only the graph. This results in an expression to be computed. This expression can then be evaluated using the available data in the estimation step.\n",
    "It is important to understand that these are orthogonal steps.\n",
    "\n",
    "#### Identification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "  d                                          \n",
      "─────(Expectation(y|W3,W4,W2,Z0,X0,W0,Z1,W1))\n",
      "d[v₀]                                        \n",
      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W4,W2,Z0,X0,W0,Z1,W1,U) = P(y|v0,W3,W4,W2,Z0,X0,W0,Z1,W1)\n",
      "\n",
      "### Estimand : 2\n",
      "Estimand name: iv\n",
      "Estimand expression:\n",
      "Expectation(Derivative(y, [Z0, Z1])*Derivative([v0], [Z0, Z1])**(-1))\n",
      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
      "\n",
      "### Estimand : 3\n",
      "Estimand name: frontdoor\n",
      "No such variable found!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n",
    "print(identified_estimand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the parameter flag *proceed\\_when\\_unidentifiable*. It needs to be set to *True* to convey the assumption that we are ignoring any unobserved confounding. The default behavior is to prompt the user to double-check that the unobserved confounders can be ignored. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "  d                                          \n",
      "─────(Expectation(y|W3,W4,W2,Z0,X0,W0,Z1,W1))\n",
      "d[v₀]                                        \n",
      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W4,W2,Z0,X0,W0,Z1,W1,U) = P(y|v0,W3,W4,W2,Z0,X0,W0,Z1,W1)\n",
      "\n",
      "## Realized estimand\n",
      "b: y~v0+W3+W4+W2+Z0+X0+W0+Z1+W1\n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 10.503496778665827\n",
      "\n",
      "Causal Estimate is 10.503496778665827\n"
     ]
    }
   ],
   "source": [
    "causal_estimate = model.estimate_effect(identified_estimand,\n",
    "        method_name=\"backdoor.propensity_score_stratification\")\n",
    "print(causal_estimate)\n",
    "print(\"Causal Estimate is \" + str(causal_estimate.value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can input additional parameters to the estimate_effect method. For instance, to estimate the effect on any subset of the units, you can specify the \"target_units\" parameter which can be a string (\"ate\", \"att\", or \"atc\"), lambda function that filters rows of the data frame, or a new dataframe on which to compute the effect. You can also specify \"effect modifiers\" to estimate heterogeneous effects across these variables. See `help(CausalModel.estimate_effect)`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "  d                                          \n",
      "─────(Expectation(y|W3,W4,W2,Z0,X0,W0,Z1,W1))\n",
      "d[v₀]                                        \n",
      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W4,W2,Z0,X0,W0,Z1,W1,U) = P(y|v0,W3,W4,W2,Z0,X0,W0,Z1,W1)\n",
      "\n",
      "## Realized estimand\n",
      "b: y~v0+W3+W4+W2+Z0+X0+W0+Z1+W1\n",
      "Target units: atc\n",
      "\n",
      "## Estimate\n",
      "Mean value: 10.490751967183876\n",
      "\n",
      "Causal Estimate is 10.490751967183876\n"
     ]
    }
   ],
   "source": [
    "# Causal effect on the control group (ATC)\n",
    "causal_estimate_att = model.estimate_effect(identified_estimand,\n",
    "        method_name=\"backdoor.propensity_score_stratification\",\n",
    "        target_units = \"atc\")\n",
    "print(causal_estimate_att)\n",
    "print(\"Causal Estimate is \" + str(causal_estimate_att.value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interface 2: Specify common causes and instruments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:dowhy.causal_model:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n"
     ]
    }
   ],
   "source": [
    "# Without graph                                       \n",
    "model= CausalModel(                             \n",
    "        data=df,                                      \n",
    "        treatment=data[\"treatment_name\"],             \n",
    "        outcome=data[\"outcome_name\"],                 \n",
    "        common_causes=data[\"common_causes_names\"],\n",
    "        effect_modifiers=data[\"effect_modifier_names\"])                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.view_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "display(Image(filename=\"causal_model.png\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get the same causal graph. Now identification and estimation is done as before.\n",
    "\n",
    "#### Identification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)                         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "  d                                 \n",
      "─────(Expectation(y|W3,W4,W2,W0,W1))\n",
      "d[v₀]                               \n",
      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W4,W2,W0,W1,U) = P(y|v0,W3,W4,W2,W0,W1)\n",
      "\n",
      "## Realized estimand\n",
      "b: y~v0+W3+W4+W2+W0+W1\n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 10.272446990065047\n",
      "\n",
      "Causal Estimate is 10.272446990065047\n"
     ]
    }
   ],
   "source": [
    "estimate = model.estimate_effect(identified_estimand,\n",
    "                                 method_name=\"backdoor.propensity_score_stratification\")         \n",
    "print(estimate)\n",
    "print(\"Causal Estimate is \" + str(estimate.value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Refuting the estimate\n",
    "\n",
    "Let us now look at ways of refuting the estimate obtained. Refutation methods provide tests that every correct estimator should pass. So if an estimator fails the refutation test (p-value is <0.05), then it means that there is some problem with the estimator. \n",
    "\n",
    "Note that we cannot verify that the estimate is correct, but we can reject it if it violates certain expected behavior (this is analogous to scientific theories that can be falsified but not proven true). The below refutation tests are based on either \n",
    " 1) **Invariant transformations**: changes in the data that should not change the estimate. Any estimator whose result varies significantly between the original data and the modified data fails the test; \n",
    " \n",
    " a) Random Common Cause\n",
    " \n",
    " b) Data Subset\n",
    " \n",
    " \n",
    " 2) **Nullifying transformations**: after the data change, the causal true estimate is zero. Any estimator whose result varies significantly from zero on the new data fails the test.\n",
    " \n",
    " a) Placebo Treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a random common cause variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Add a random common cause\n",
      "Estimated effect:10.272446990065047\n",
      "New effect:10.22855222425012\n",
      "p value:0.28\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_random=model.refute_estimate(identified_estimand, estimate, method_name=\"random_common_cause\")\n",
    "print(res_random)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacing treatment with a random (placebo) variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Use a Placebo Treatment\n",
      "Estimated effect:10.272446990065047\n",
      "New effect:0.014834254443949355\n",
      "p value:0.44\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_placebo=model.refute_estimate(identified_estimand, estimate,\n",
    "        method_name=\"placebo_treatment_refuter\", placebo_type=\"permute\")\n",
    "print(res_placebo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing a random subset of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Use a subset of data\n",
      "Estimated effect:10.272446990065047\n",
      "New effect:10.231666326841792\n",
      "p value:0.31999999999999995\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_subset=model.refute_estimate(identified_estimand, estimate,\n",
    "        method_name=\"data_subset_refuter\", subset_fraction=0.9)\n",
    "print(res_subset)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the propensity score stratification estimator is reasonably robust to refutations.\n",
    "For reproducibility, you can add a parameter \"random_seed\" to any refutation method, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Use a subset of data\n",
      "Estimated effect:10.272446990065047\n",
      "New effect:10.245311785764631\n",
      "p value:0.36\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_subset=model.refute_estimate(identified_estimand, estimate,\n",
    "        method_name=\"data_subset_refuter\", subset_fraction=0.9, random_seed = 1)\n",
    "print(res_subset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding an unobserved common cause variable\n",
    "\n",
    "This  refutation does not return a p-value. Instead, it provides a _sensitivity_ test on how quickly the estimate changes if the identifying assumptions (used in `identify_effect`) are not valid. Specifically, it checks sensitivity to violation of the  backdoor assumption: that all common causes are observed. \n",
    "\n",
    "To do so, it creates a new dataset with an additional common cause between treatment and outcome. To capture the effect of the common cause, the method takes as input the strength of common cause's effect on treatment and outcome. Based on these inputs on the common cause's effects, it changes the treatment and outcome values and then reruns the estimator. The hope is that the new estimate does not change drastically with a small effect of the unobserved common cause, indicating a robustness to any unobserved confounding.\n",
    "\n",
    "Another equivalent way of interpreting this procedure is to assume that there was already unobserved confounding present in the input data. The change in treatment and outcome values _removes_ the effect of whatever unobserved common cause was present in the original data. Then rerunning the estimator on this modified data provides the correct identified estimate and we hope that the difference between the new estimate and the original estimate is not too high, for some bounded value of the unobserved common cause's effect.\n",
    "\n",
    "**Importance of domain knowledge**: This test requires _domain knowledge_ to set plausible input values of the effect of unobserved confounding. We first show the result for a single value of confounder's effect on treatment and outcome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Add an Unobserved Common Cause\n",
      "Estimated effect:10.272446990065047\n",
      "New effect:9.372739046931116\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_unobserved=model.refute_estimate(identified_estimand, estimate, method_name=\"add_unobserved_common_cause\",\n",
    "                                     confounders_effect_on_treatment=\"binary_flip\", confounders_effect_on_outcome=\"linear\",\n",
    "                                    effect_strength_on_treatment=0.01, effect_strength_on_outcome=0.02)\n",
    "print(res_unobserved)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is often more useful to inspect the trend as the effect of unobserved confounding is increased. For that, we can provide an array of hypothesized confounders' effects. The output is the *(min, max)* range of the estimated effects under different unobserved confounding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Add an Unobserved Common Cause\n",
      "Estimated effect:10.272446990065047\n",
      "New effect:(7.739232072447066, 10.01948990996014)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_unobserved_range=model.refute_estimate(identified_estimand, estimate, method_name=\"add_unobserved_common_cause\",\n",
    "                                     confounders_effect_on_treatment=\"binary_flip\", confounders_effect_on_outcome=\"linear\",\n",
    "                                    effect_strength_on_treatment=np.array([0.001, 0.005, 0.01, 0.02]), effect_strength_on_outcome=0.01)\n",
    "print(res_unobserved_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows how the estimate decreases as the hypothesized confounding on treatment increases. By domain knowledge, we may know the maximum plausible confounding effect on treatment. Since we see that the effect does not go beyond zero, we can safely conclude that the causal effect of treatment `v0` is positive.\n",
    "\n",
    "We can also vary the confounding effect on both treatment and outcome. We obtain a heatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Add an Unobserved Common Cause\n",
      "Estimated effect:10.272446990065047\n",
      "New effect:(4.360610187327535, 10.041829912816333)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_unobserved_range=model.refute_estimate(identified_estimand, estimate, method_name=\"add_unobserved_common_cause\",\n",
    "                                           confounders_effect_on_treatment=\"binary_flip\", confounders_effect_on_outcome=\"linear\",\n",
    "                                           effect_strength_on_treatment=[0.001, 0.005, 0.01, 0.02], \n",
    "                                           effect_strength_on_outcome=[0.001, 0.005, 0.01,0.02])\n",
    "print(res_unobserved_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Automatically inferring effect strength parameters.** Finally, DoWhy supports automatic selection of the effect strength parameters. This is based on an assumption that the effect of the unobserved confounder on treatment or outcome cannot be stronger than that of any observed confounder. That is, we have collected data at least for the most relevant confounder. If that is the case, then we can bound the range of `effect_strength_on_treatment` and `effect_strength_on_outcome` by the effect strength of observed confounders. There is an additional optional parameter signifying whether the effect strength of unobserved confounder should be as high as the highest observed, or a fraction of it. You can set it using the optional `effect_fraction_on_treatment` and `effect_fraction_on_outcome` parameters. By default, these two parameters are 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Add an Unobserved Common Cause\n",
      "Estimated effect:10.272446990065047\n",
      "New effect:(2.433189098709768, 9.990182340012971)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_unobserved_auto = model.refute_estimate(identified_estimand, estimate, method_name=\"add_unobserved_common_cause\",\n",
    "                                           confounders_effect_on_treatment=\"binary_flip\", confounders_effect_on_outcome=\"linear\")\n",
    "print(res_unobserved_auto)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**: Assuming that the unobserved confounder does not affect the treatment or outcome more strongly than any observed confounder, the causal effect can be concluded to be positive."
   ]
  }
 ],
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   "language": "python",
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    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "toc": {
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  }
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